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Ideas for Fault Detection Using Relation Discovery
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
Volvo Group Trucks Technology, Göteborg, Sweden.ORCID iD: 0000-0001-8255-1276
2012 (English)In: / [ed] Lars Karlsson and Julien Bidot, Linköping: Linköping University Electronic Press, 2012, 1-6 p.Conference paper, Presentation (Refereed)
Abstract [en]

Predictive maintenance is becoming more and more important in many industries, especially taking into account the increasing focus on offering uptime guarantees to the customers. However, in automotive industry, there is a limitation on the engineering effort and sensor capabilities available for that purpose. Luckily, it has recently become feasible to analyse large amounts of data on-board vehicles in a timely manner. This allows approaches based on data mining and pattern recognition techniques to augment existing, hand crafted algorithms.

Automated deviation detection offers both broader applicability, by virtue of detecting unexpected faults and cross-analysing data from different subsystems, as well as higher sensitivity, due to its ability to take into account specifics of a selected, small set of vehicles used in a particular way under similar conditions.

In a project called Redi2Service we work towards developing methods for autonomous and unsupervised relationship discovery, algorithms for detecting deviations within those relationships (both considering different moments in time, and different vehicles in a fleet), as well as ways to correlate those deviations to known and unknown faults. In this paper we present the type of data we are working with, justify why we believe relationships between signals are a good knowledge representation, and show results of early experiments where supervised learning was used to evaluate discovered relations.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 1-6 p.
, Linköping Electronic Conference Proceedings, ISSN 1650-3740 ; 071
National Category
Computer Science
URN: urn:nbn:se:hh:diva-17718OAI: diva2:528136
The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS), 14–15 May 2012, Örebro, Sweden
Available from: 2012-05-25 Created: 2012-05-24 Last updated: 2015-02-12Bibliographically approved

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